Tools

"... This paper describes how evolutionary techniques of variation and selection can be used to create complex simulated structures, textures, and motions for use in computer graphics and animation. Interactive selection, based on visual perception of procedurally generated results, allows the user to di ..."

This paper describes how evolutionary techniques of variation and selection can be used to create complex simulated structures, textures, and motions for use in computer graphics and animation. Interactive selection, based on visual perception of procedurally generated results, allows the user to direct simulated evolutions in preferred directions. Several examples using these methods have been implemented and are described. 3D plant structures are grown using fixed sets of genetic parameters. Images, solid textures, and animations are created using mutating symbolic lisp expressions. Genotjps consisting of symbolic expressions are presented as an attempt to surpass the limitations of fixed-length genotypes with predefine expression rules. his proposed that artificial evolution has potential as a powerful tool for achieving flexible complexity with a minimum of user input and knowledge of details. 2

"... In this paper we propose and justify a methodology for the development of the control systems, or `cognitive architectures', of autonomous mobile robots. We argue that the design by hand of such control systems becomes prohibitively difficult as complexity increases. We discuss an alternative a ..."

In this paper we propose and justify a methodology for the development of the control systems, or `cognitive architectures&apos;, of autonomous mobile robots. We argue that the design by hand of such control systems becomes prohibitively difficult as complexity increases. We discuss an alternative approach, involving artificial evolution, where the basic building blocks for cognitive architectures are adaptive noise-tolerant dynamical neural networks, rather than programs. These networks may be recurrent, and should operate in real time. Evolution should be incremental, using an extended and modified version of genetic algorithms. We nally propose that, sooner rather than later, visual processing will be required in order for robots to engage in non-trivial navigation behaviours. Time constraints suggest that initial architecture evaluations should be largely done in simulation. The pitfalls of simulations compared with reality are discussed, together with the importance of incorporating noise. To support our claims and proposals, we present results from some preliminary experiments where robots which roam office-like environments are evolved.

...ed on top of them. Furthermore, there are other evolutionary schemes that allow for variable-dimensionality search spaces, of which probably the best-known is Koza’s form of evolutionary programming (=-=Koza, 1990-=-, 1992). The differences between these two approaches are discussed in the next section. 2.5 What should we evolve? Thus far we have not fully addressed the question of why we are evolving controllers...

"... This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnet ..."

This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. A genetic algorithm is used to evolve coded grammars that generates ANNs for controlling a six-legged robot locomotion. A mechanism for the automatic definition of sub-neural networks is incorporated. Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic definition of sub-networks is suppressed. We support our argumentation with pictures describing the steps of development, how ANN structures ar...

"... Abstract. What makes a problem easy or hard for a genetic algorithm (GA)? This question has become increas-ingly important as people have tried to apply the GA to ever more diverse types of problems. Much previous work on this question has studied the relationship between GA performance and the stru ..."

Abstract. What makes a problem easy or hard for a genetic algorithm (GA)? This question has become increas-ingly important as people have tried to apply the GA to ever more diverse types of problems. Much previous work on this question has studied the relationship between GA performance and the structure of a given fitness function when it is expressed as a Walsh polynomial. The work of Bethke, Goldberg, and others has produced certain theoretical results about this relationship. In this article we review these theoretical results, and then dis-cuss a number of seemingly anomalous experimental results reported by Tanese concerning the performance of the GA on a subclass of Walsh polynomials, some members of which were expected to be easy for the GA to optimize. Tanese found that the GA was poor at optimizing all functions in this subclass, that a partitioning of a single large population into a number of smaller independent populations seemed to improve performance, and that hillclimbing outperformed both the original and partitioned forms of the GA on these functions. These results seemed to contradict several commonly held expectations about GAs. We begin by reviewing schema processing in GAs. We then give an informal description of how Walsh analysis and Bethke&apos;s Walsh-schema transform relate to GA performance, and we discuss the relevance of this analysis for GA applications in optimization and machine learning. We then describe Tanese&apos;s surprising results, examine them experimentally and theoretically, and propose and evaluate some explanations. These explanations lead to a more fundamental question about GAs: what are the features of problems that determine the likelihood of suc-cessful GA performance?

"... For Artificial Life applications it is useful to extend Genetic Algorithms from a finite search space with fixed-length genotypes to open-ended evolution with variable-length genotypes. A new theoretical analysis is required, as Holland's Schema Theorem only applies to fixed lengths. It will be ..."

For Artificial Life applications it is useful to extend Genetic Algorithms from a finite search space with fixed-length genotypes to open-ended evolution with variable-length genotypes. A new theoretical analysis is required, as Holland&apos;s Schema Theorem only applies to fixed lengths. It will be argued, using concepts of epistasis and fitness landscapes drawn from theoretical biology, that in the long run a population must havegenotypes of nearly equal length, and this length can only increase slowly. As the length increases, the population will be nearly converged, and hence evolving as a species.

by
John R. Koza, James P. Rice
- In International Joint Conference on Neural Networks, 1991

"... ABSTRACT: This paper shows how to find both the weights and architecture for a neural network (including the number of layers, the number of processing elements per layer, and the connectivity between processing elements). This is accomplished using a recently developed extension to the genetic algo ..."

ABSTRACT: This paper shows how to find both the weights and architecture for a neural network (including the number of layers, the number of processing elements per layer, and the connectivity between processing elements). This is accomplished using a recently developed extension to the genetic algorithm which genetically breeds a population of LISP symbolic expressions (S-expressions) of varying size and shape until the desired performance by the network is successfully evolved. The new &amp;quot;genetic programming &amp;quot; paradigm is applied to the problem of generating a neural network for the one-bit adder. 1.

...more than one place in the tree. Thus, it is possible to create connectivity between any input data signal and any number of processing elements. Similarly, thes&quot;define building block&quot; operation (see =-=Koza 1990-=- for details) makes it possible to have connectivity between the output from any linear processing element function P in the network and the inputs of other linear processing element functions. &quot;Defin...

"... Forma analysis is applied to the task of optimising the connectivity of a feed-forward neural network with a single layer of hidden units. This problem is reformulated as a multiset optimisation problem and techniques are developed to allow principled genetic search over fixed- and variable-si ..."

Forma analysis is applied to the task of optimising the connectivity of a feed-forward neural network with a single layer of hidden units. This problem is reformulated as a multiset optimisation problem and techniques are developed to allow principled genetic search over fixed- and variable-size sets and multisets. These techniques require a further generalisation of the notion of gene, which is presented. The result is a non-redundant representation of the neural network topology optimisation problem together with recombination operators which have carefully designed and well-understood properties. The techniques developed have relevance to the application of genetic algorithms to constrained optimisation problems.

"... this paper for descriptive purposes only. The co-evolution algorithm uses only relative fitness. In one run (with population size of 300), the individual strategy for player X in the initial random generation (generation 0) with the best relative fitness was ..."

this paper for descriptive purposes only. The co-evolution algorithm uses only relative fitness. In one run (with population size of 300), the individual strategy for player X in the initial random generation (generation 0) with the best relative fitness was

"... This paper will look at an evolutionary approach to robotics; partly at pragmatic issues, but primarily at theoretical issues associated with the evolutionary algorithms which are appropriate. Genetic Algorithms are not suitable in their usual form for the evolution of cognitive structures, which mu ..."

This paper will look at an evolutionary approach to robotics; partly at pragmatic issues, but primarily at theoretical issues associated with the evolutionary algorithms which are appropriate. Genetic Algorithms are not suitable in their usual form for the evolution of cognitive structures, which must be in an incremental fashion. SAGA -- Species Adaptation Genetic Algorithms -- is a conceptual framework for extending GAs to variable length genotypes, where evolution allows a species of individuals to evolve from simple to more complex. In the context of species evolution the metaphor of hill-crawling as opposed to hill-climbing is introduced,